CITEseq marker Differential
February 2022
1 Pre-processing
1.1 Load packages
library(Seurat)
library(Signac)
library(flexclust)
library(tidyverse)
library(plyr)
library(harmony)
library(class)
library(ggplot2)
library(reshape2)1.2 Parameters
path_to_save_annotated_citeseq <- "/home/srashmi/Documents/tonsil_atlas_citeseq_vdj_20210505/10-sub_cluster_annotation/PC_differential/objects/PC_tonsil_cite_seq_annotated.rds"
color <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "mediumorchid2", "coral2", "blueviolet",
"indianred4", "deepskyblue1", "dimgray", "deeppink1",
"greenyellow", "lightgray", "hotpink1", "chocolate", "aquamarine", "aliceblue", "burlywood", "blueviolet", "indianred", "gold","coral", "orangered")1.3 Variables
1.4 Load data
seurat_citeseq <- readRDS(path_to_save_annotated_citeseq)
DefaultAssay(seurat_citeseq) <- "ADT"
Idents(seurat_citeseq) <- seurat_citeseq@meta.data$PC_annotation_level_51.5 GZ-LZ vs PC commited
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "GC-LZ", ident.2 = "PC committed GC-LZ", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F)
## $`0`
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1.6 GZ-LZ vs IgG+ PC precursor
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "GC-LZ", ident.2 = "IgG+ PC precursor", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
1.7 GZ-LZ vs Pre-Mature IgG+ PC
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "GC-LZ", ident.2 = "Pre-Mature IgG+ PC", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
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1.8 GZ-LZ vs Mature PC (“MBC-derived IgG+ PC”, “MBC-derived IgA+ PC”, “Mature IgM+ PC”, “Mature IgG+ PC”, “Mature IgA+ PC”)
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "GC-LZ", ident.2 = c("MBC-derived IgG+ PC", "MBC-derived IgA+ PC", "Mature IgM+ PC", "Mature IgG+ PC", "Mature IgA+ PC"), min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
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1.9 PC committed GC-LZ vs IgG+ PC precursor
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "PC committed GC-LZ", ident.2 = "IgG+ PC precursor", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
1.10 PC committed GC-LZ vs Pre-Mature IgG+ PC
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "PC committed GC-LZ", ident.2 = "Pre-Mature IgG+ PC", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
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## $`3`
1.11 PC committed GC-LZ vs Mature PC (“MBC-derived IgG+ PC”, “MBC-derived IgA+ PC”, “Mature IgM+ PC”, “Mature IgG+ PC”, “Mature IgA+ PC”)
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "PC committed GC-LZ", ident.2 = c("MBC-derived IgG+ PC", "MBC-derived IgA+ PC", "Mature IgM+ PC", "Mature IgG+ PC", "Mature IgA+ PC"), min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
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## $`3`
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## $`4`
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## $`5`
1.12 IgG+ PC precursor vs Pre-Mature IgG+ PC
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "IgG+ PC precursor", ident.2 = "Pre-Mature IgG+ PC", min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
1.13 IgG+ PC precursor vs Mature PC (“MBC-derived IgG+ PC”, “MBC-derived IgA+ PC”, “Mature IgM+ PC”, “Mature IgG+ PC”, “Mature IgA+ PC”)
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "IgG+ PC precursor", ident.2 = c("MBC-derived IgG+ PC", "MBC-derived IgA+ PC", "Mature IgM+ PC", "Mature IgG+ PC", "Mature IgA+ PC"), min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F)
## $`0`
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## $`1`
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## $`2`
1.14 Pre-Mature IgG+ PC vs Mature PC (“MBC-derived IgG+ PC”, “MBC-derived IgA+ PC”, “Mature IgM+ PC”, “Mature IgG+ PC”, “Mature IgA+ PC”)
markers <- FindMarkers(object = seurat_citeseq, ident.1 = "Pre-Mature IgG+ PC", ident.2 = c("MBC-derived IgG+ PC", "MBC-derived IgA+ PC", "Mature IgM+ PC", "Mature IgG+ PC", "Mature IgA+ PC"), min.pct = 0.25)
DT::datatable(markers)DoHeatmap(seurat_citeseq, features = unique(rownames(markers)), label = F) 
## $`0`
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## $`1`
2 Session Information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] reshape2_1.4.4 class_7.3-18 harmony_0.1.0 Rcpp_1.0.7 plyr_1.8.6 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 flexclust_1.4-0 modeltools_0.2-23 lattice_0.20-41 Signac_1.5.0 SeuratObject_4.0.4 Seurat_4.0.5 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] SnowballC_0.7.0 scattermore_0.7 knitr_1.36 irlba_2.3.5 data.table_1.14.2 rpart_4.1-15 RCurl_1.98-1.5 generics_0.1.1 BiocGenerics_0.36.1 cowplot_1.1.1 RANN_2.6.1 future_1.23.0 tzdb_0.2.0 spatstat.data_2.1-0 xml2_1.3.3 lubridate_1.8.0 httpuv_1.6.4 assertthat_0.2.1 xfun_0.29 hms_1.1.1 jquerylib_0.1.4 evaluate_0.14 promises_1.2.0.1 fansi_0.5.0 dbplyr_2.1.1 readxl_1.3.1 igraph_1.2.6 DBI_1.1.1 htmlwidgets_1.5.4 sparsesvd_0.2 spatstat.geom_2.3-1 ellipsis_0.3.2 crosstalk_1.2.0 backports_1.4.1 bookdown_0.24 deldir_1.0-6 vctrs_0.3.8 ROCR_1.0-11 abind_1.4-5 withr_2.4.3 ggforce_0.3.3 sctransform_0.3.2.9008 goftest_1.2-3 cluster_2.1.0 lazyeval_0.2.2 crayon_1.4.2 pkgconfig_2.0.3 slam_0.1-49 labeling_0.4.2 tweenr_1.0.2 GenomeInfoDb_1.26.7
## [52] nlme_3.1-152 rlang_0.4.12 globals_0.14.0 lifecycle_1.0.1 miniUI_0.1.1.1 modelr_0.1.8 cellranger_1.1.0 polyclip_1.10-0 matrixStats_0.61.0 lmtest_0.9-39 Matrix_1.4-0 ggseqlogo_0.1 zoo_1.8-9 reprex_2.0.1 ggridges_0.5.3 png_0.1-7 viridisLite_0.4.0 bitops_1.0-7 KernSmooth_2.23-18 Biostrings_2.58.0 parallelly_1.29.0 S4Vectors_0.28.1 scales_1.1.1 magrittr_2.0.1 ica_1.0-2 zlibbioc_1.36.0 compiler_4.0.3 RColorBrewer_1.1-2 fitdistrplus_1.1-6 Rsamtools_2.6.0 cli_3.1.0 XVector_0.30.0 listenv_0.8.0 patchwork_1.1.1 pbapply_1.5-0 MASS_7.3-54 mgcv_1.8-33 tidyselect_1.1.1 stringi_1.7.6 highr_0.9 yaml_2.2.1 ggrepel_0.9.1 sass_0.4.0 fastmatch_1.1-3 tools_4.0.3 future.apply_1.8.1 parallel_4.0.3 rstudioapi_0.13 lsa_0.73.2 gridExtra_2.3 farver_2.1.0
## [103] Rtsne_0.15 digest_0.6.29 BiocManager_1.30.16 shiny_1.7.1 qlcMatrix_0.9.7 GenomicRanges_1.42.0 broom_0.7.10 later_1.3.0 RcppAnnoy_0.0.19 httr_1.4.2 colorspace_2.0-2 rvest_1.0.2 fs_1.5.2 tensor_1.5 reticulate_1.22 IRanges_2.24.1 splines_4.0.3 uwot_0.1.11 RcppRoll_0.3.0 spatstat.utils_2.3-0 plotly_4.10.0 xtable_1.8-4 jsonlite_1.7.2 R6_2.5.1 pillar_1.6.4 htmltools_0.5.2 mime_0.12 glue_1.5.1 fastmap_1.1.0 DT_0.20 BiocParallel_1.24.1 codetools_0.2-18 utf8_1.2.2 bslib_0.3.1 spatstat.sparse_2.0-0 leiden_0.3.9 survival_3.2-7 limma_3.46.0 rmarkdown_2.11 docopt_0.7.1 munsell_0.5.0 GenomeInfoDbData_1.2.4 haven_2.4.3 gtable_0.3.0 spatstat.core_2.3-2